Got it 👍 You want me to completely rebuild your documentation into a polished README-style format — covering functional flows, technical stack, security, libraries (like ZXing for QR), and deployment notes, without missing anything.
Here’s a remade version that’s hackathon-ready and production-oriented:
Offline-First | Real-Time | AI-Driven | Human-Centered
A hybrid mobile + control room platform for flood and disaster mitigation, designed to work in online + offline conditions.
- Centralized AI Insights: Flood prediction, rescue prioritization, safe-zone recommendations
- Offline Mesh Communication: BLE + Wi-Fi mesh + LoRa (gateway/emulated) for SOS & hazard propagation
- Human-Centric Features: SOS alerts, family tracking, safe house management, hazard/waste reporting
- Bidirectional Data Flow:
- Field → Control Room: SOS, DigiPIN, family status, hazard reports, safe house check-ins
- Control Room → Field: AI predictions, safe-zone updates, verified hazard assignments
- SOS Button: Generates DigiPIN + GPS, broadcast via mesh
- SOS Reception & Rescue: Accept & mark
in-progress/rescued - Family Grouping: Track family status (safe, SOS, rescued)
- Safe House Check-in: QR code (generated via ZXing) scanned by rescuer/admin
- Hazard/Waste Reporting: Upload photo + location + description → ML verification
- Offline-first: Local cache, auto-sync when online
- Privacy-Friendly: Names/initials only, sensitive info hidden
- Accessibility: Multilingual & voice (voice-to-text SOS, text-to-speech alerts)
- Receive & accept SOS requests
- Forward mesh data offline → sync online when available
- Manage safe houses: register, assign admins, scan QR check-ins
- Handle hazard reports: accept tasks, upload post-action photos
- Use AI insights: prioritization (family clusters, flood risk, battery, location)
- Offline functionality
- Dashboard: Live SOS map, DigiPINs, family clusters, safe houses, hazards
- Safe House Management: Track occupancy, family clusters, admins
- Hazard Verification: ML-assisted verification, assignment to rescuers, track status
- AI Analytics:
- Flood risk prediction
- Rescue prioritization
- Safe-zone & bottleneck forecasting
- SOS clustering
- Bridges mesh → cloud (Convex + FastAPI)
- Receives SOS/hazard → forwards for canonical processing
- Emulates long-range offline communication in demo
- Civilian presses SOS → DigiPIN generated → broadcast via BLE/Wi-Fi mesh
- Nearby devices receive → may accept or forward
- Forwarding continues offline until internet node (rescuer or LoRa gateway) is reached
- Backend (Convex): assigns canonical
sos_id+ stores hop trace - AI (FastAPI): computes rescue priority score
- Status updates (
in-progress,rescued) sync back across devices
- Rescuer registers new safe house → backend logs it
- User shows QR code (ZXing) → rescuer/admin scans → marks safe
- Safe House Page: shows all occupants (family on top)
- Dashboard: real-time occupancy, family grouping
- User submits report: photo + location + description
- FastAPI ML verifies (CNN/CLIP model for hazard classification)
- Verified → assigned to rescuer → marked
In Progress - Rescuer completes → uploads post-action photo → optional ML recheck
- Updates propagate back to user & dashboard
- Integrated with APIs:
- OpenWeatherMap (rainfall, river levels)
- NOAA / Copernicus EMS / USGS (flood + earthquake + landslide data)
- AI Models:
- LSTM/RNN → rainfall-to-flood prediction
- Clustering → SOS density hot-zones
- Graph shortest path → safe-zone navigation
| Layer | Technology |
|---|---|
| Core Backend | Convex Database + Functions → Real-time sync, SOS, family, safe houses |
| AI/ML Services | FastAPI + Python ML Models → Hazard verification, flood prediction |
| Mobile App | React Native + Convex SDK + ZXing (QR code generation & scanning) |
| Dashboard | Next.js + Convex Subscriptions + Mapbox/Leaflet for maps |
| Communication | BLE/Wi-Fi mesh (offline), LoRa gateway (emulated laptop with FastAPI) |
| Realtime Sync | Convex Live Queries (low-latency pub/sub) |
| Storage | Convex (core), S3/Cloudinary (media uploads), SQLite (local offline cache) |
- Authentication: Convex Auth (JWT + OAuth providers)
- QR Security: ZXing QR codes signed with backend tokens (prevent spoofing)
- Role-Based Access Control: Civilian / Rescuer / Admin
- Data Privacy: Minimal info shared (initials only for safe houses)
- Encryption:
- End-to-end encrypted SOS messages
- TLS for API & Convex traffic
- Convex: Auto-scaled backend with global edge sync
- FastAPI: Deploy via Docker → Railway/Fly.io/Render (serverless or containerized)
- Mobile App: Expo + React Native → Android/iOS build
- Dashboard: Vercel/Netlify → auto-deploy Next.js
- LoRa Gateway: Python FastAPI service on laptop with BLE/Wi-Fi forwarders
(Suggested Visual)
[Civilian App] <---> [Mesh: BLE/WiFi] <---> [Rescuer App] <---> [LoRa Gateway Laptop]
| |
| v
| [FastAPI AI/ML]
| |
v v
-------------------------- Real-time Sync -------------------------
| Convex Cloud | <-----------------> | Next.js Admin Panel |
-------------------------- -------------------------
- Convex + FastAPI hybrid = real-time + AI power
- ZXing QR integration = secure safe house check-ins
- Mesh + LoRa demo = offline-first resilience
- AI-verified hazard flow = ML adds trustworthiness
- Clear privacy rules = data minimalism respected
🔥 With this README-style documentation, you’ve got functional + technical clarity. It will impress both judges (concept clarity) and tech reviewers (stack clarity).
Do you want me to also generate a polished architecture diagram image (instead of ASCII), so you can drop it straight into your README?